Patrick Rubin‐Delanchy

890 total citations
24 papers, 440 citations indexed

About

Patrick Rubin‐Delanchy is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Biophysics. According to data from OpenAlex, Patrick Rubin‐Delanchy has authored 24 papers receiving a total of 440 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 8 papers in Statistical and Nonlinear Physics and 7 papers in Biophysics. Recurrent topics in Patrick Rubin‐Delanchy's work include Complex Network Analysis Techniques (7 papers), Advanced Fluorescence Microscopy Techniques (7 papers) and Cell Image Analysis Techniques (5 papers). Patrick Rubin‐Delanchy is often cited by papers focused on Complex Network Analysis Techniques (7 papers), Advanced Fluorescence Microscopy Techniques (7 papers) and Cell Image Analysis Techniques (5 papers). Patrick Rubin‐Delanchy collaborates with scholars based in United Kingdom, United States and Switzerland. Patrick Rubin‐Delanchy's co-authors include Nicholas A. Heard, Andrew T. Walden, Juliette Griffié, Dylan M. Owen, David J. Williamson, Garth L. Burn, Andrew P. Cope, Carey E. Priebe, Minh Tang and Joshua Cape and has published in prestigious journals such as Nature Communications, Journal of the American Statistical Association and Technometrics.

In The Last Decade

Patrick Rubin‐Delanchy

23 papers receiving 427 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Patrick Rubin‐Delanchy United Kingdom 11 149 112 86 60 54 24 440
Nicholas A. Heard United Kingdom 9 106 0.7× 227 2.0× 154 1.8× 45 0.8× 29 0.5× 33 563
Edward A. K. Cohen United Kingdom 15 136 0.9× 83 0.7× 82 1.0× 62 1.0× 20 0.4× 52 644
Xiuyuan Cheng United States 10 15 0.1× 75 0.7× 65 0.8× 8 0.1× 25 0.5× 42 413
Vasileios Maroulas United States 13 27 0.2× 57 0.5× 115 1.3× 5 0.1× 11 0.2× 57 582
Adam M. Johansen United Kingdom 14 25 0.2× 80 0.7× 346 4.0× 8 0.1× 27 0.5× 42 709
Zachary T. Harmany United States 12 178 1.2× 65 0.6× 57 0.7× 19 0.3× 41 0.8× 29 785
Morteza Shahram United States 11 46 0.3× 29 0.3× 67 0.8× 5 0.1× 73 1.4× 18 492
Keegan Hines United States 8 50 0.3× 132 1.2× 100 1.2× 6 0.1× 7 0.1× 12 336
Ben van Werkhoven Netherlands 11 69 0.5× 43 0.4× 58 0.7× 48 0.8× 9 0.2× 42 408

Countries citing papers authored by Patrick Rubin‐Delanchy

Since Specialization
Citations

This map shows the geographic impact of Patrick Rubin‐Delanchy's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Patrick Rubin‐Delanchy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick Rubin‐Delanchy more than expected).

Fields of papers citing papers by Patrick Rubin‐Delanchy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Patrick Rubin‐Delanchy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Patrick Rubin‐Delanchy. The network helps show where Patrick Rubin‐Delanchy may publish in the future.

Co-authorship network of co-authors of Patrick Rubin‐Delanchy

This figure shows the co-authorship network connecting the top 25 collaborators of Patrick Rubin‐Delanchy. A scholar is included among the top collaborators of Patrick Rubin‐Delanchy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Patrick Rubin‐Delanchy. Patrick Rubin‐Delanchy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Nieves, Daniel J., Jeremy A. Pike, Mahmoud A. Ahmed, et al.. (2025). Nano-org, a functional resource for single-molecule localisation microscopy data. Nature Communications. 16(1). 8674–8674.
2.
Gallagher, Ian, et al.. (2023). Spectral Embedding of Weighted Graphs. Journal of the American Statistical Association. 119(547). 1923–1932. 2 indexed citations
3.
Jensen, L G, David J. Williamson, Juliette Griffié, et al.. (2022). Correction of multiple-blinking artifacts in photoactivated localization microscopy. Nature Methods. 19(5). 594–602. 21 indexed citations
4.
Heard, Nicholas A., et al.. (2021). Spectral Clustering on Spherical Coordinates Under the Degree-Corrected Stochastic Blockmodel. Technometrics. 64(3). 346–357. 4 indexed citations
5.
Adams, Niall M., et al.. (2018). Data Science for Cyber-Security. 9 indexed citations
6.
Griffié, Juliette, Garth L. Burn, David J. Williamson, et al.. (2018). Dynamic Bayesian Cluster Analysis of Live‐Cell Single Molecule Localization Microscopy Datasets. Small Methods. 2(9). 13 indexed citations
7.
Rubin‐Delanchy, Patrick, Nicholas A. Heard, & Daniel J. Lawson. (2018). Meta-Analysis of Mid- p -Values: Some New Results based on the Convex Order. Journal of the American Statistical Association. 114(527). 1105–1112. 10 indexed citations
8.
Rubin‐Delanchy, Patrick, Carey E. Priebe, & Minh Tang. (2017). The generalised random dot product graph. arXiv (Cornell University). 1 indexed citations
9.
Griffié, Juliette, Dylan M. Owen, Patrick Rubin‐Delanchy, & Garth L. Burn. (2017). Quantitative Analysis of Membrane Protein Clustering from Live-Cell, Single-Molecule Super-Resolution Microscopy Data. Biophysical Journal. 112(3). 144a–145a. 1 indexed citations
10.
Griffié, Juliette, David J. Williamson, Michael J. Shannon, et al.. (2017). 3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse. Scientific Reports. 7(1). 4077–4077. 21 indexed citations
11.
Heard, Nicholas A. & Patrick Rubin‐Delanchy. (2017). Choosing between methods of combining $p$-values. Biometrika. 105(1). 239–246. 86 indexed citations
12.
Griffié, Juliette, Michael J. Shannon, Lies Boelen, et al.. (2016). A Bayesian cluster analysis method for single-molecule localization microscopy data. Nature Protocols. 11(12). 2499–2514. 38 indexed citations
13.
Rubin‐Delanchy, Patrick, et al.. (2016). Network-wide anomaly detection via the Dirichlet process. Spiral (Imperial College London). 220–224. 11 indexed citations
14.
Gandy, Axel & Patrick Rubin‐Delanchy. (2016). AN ALGORITHM TO COMPUTE THE POWER OF MONTE CARLO TESTS WITH GUARANTEED PRECISION1. 3 indexed citations
15.
Rubin‐Delanchy, Patrick, Niall M. Adams, & Nicholas A. Heard. (2016). Disassortativity of computer networks. Spiral (Imperial College London). 20. 243–247. 4 indexed citations
16.
Rubin‐Delanchy, Patrick, Garth L. Burn, Juliette Griffié, et al.. (2015). Bayesian cluster identification in single-molecule localization microscopy data. Nature Methods. 12(11). 1072–1076. 91 indexed citations
17.
Rubin‐Delanchy, Patrick, et al.. (2014). Filtering Automated Polling Traffic in Computer Network Flow Data. Bristol Research (University of Bristol). 268–271. 15 indexed citations
18.
Rubin‐Delanchy, Patrick & Andrew T. Walden. (2008). Kinematics of Complex-Valued Time Series. IEEE Transactions on Signal Processing. 56(9). 4189–4198. 15 indexed citations
19.
Walden, Andrew T. & Patrick Rubin‐Delanchy. (2008). On Testing for Impropriety of Complex-Valued Gaussian Vectors. IEEE Transactions on Signal Processing. 57(3). 825–834. 43 indexed citations
20.
Rubin‐Delanchy, Patrick & Andrew T. Walden. (2007). Simulation of Improper Complex-Valued Sequences. IEEE Transactions on Signal Processing. 55(11). 5517–5521. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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